Merge branch 'refs/heads/dev-xym-add-feature' into pre-master

ad-engine-commons/src/main/java/com/tzld/piaoquan/ad/engine/commons/score/model/PAIModelV1.java

@@ -33,7 +33,7 @@ public class PAIModelV1 {
     static {
         model = new PAIModelV1();
         client = new PredictClient(new HttpConfig());
-        client.setEndpoint("1894469520484605.cn-hangzhou.pai-eas.aliyuncs.com");
+        client.setEndpoint("1894469520484605.vpc.cn-hangzhou.pai-eas.aliyuncs.com");
         client.setToken("ODI1MmUxODgzZDc3ODM0ZmQwZWU0YTVjZjdlOWVlMGFlZGJjNTlkYQ==");
         client.setModelName("ad_rank_dnn_v11_easyrec");
     }
@@ -43,7 +43,7 @@ public class PAIModelV1 {
             "user_vid_return_tags_2h", "user_vid_return_tags_1d", "user_vid_return_tags_3d", "user_vid_return_tags_7d",
             "user_vid_return_tags_14d", "root_source_scene", "root_source_channel", "title_split", "user_vid_share_tags_1d",
             "user_vid_share_tags_14d", "user_vid_return_cate1_14d", "user_vid_return_cate2_14d", "user_vid_share_cate1_14d",
-            "user_vid_share_cate2_14d", "user_conver_ad_class"
+            "user_vid_share_cate2_14d", "user_conver_ad_class", "user_layer_class"
     };
 
     private static final String[] sparseUserLongFeatures = {

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/predict/model/threshold/RandomPredict667Model.java

@@ -1,5 +1,7 @@
 package com.tzld.piaoquan.ad.engine.service.predict.model.threshold;
 
+import com.alibaba.fastjson.JSONObject;
+import com.tzld.piaoquan.ad.engine.commons.util.JSONUtils;
 import com.tzld.piaoquan.ad.engine.service.predict.container.RandWContainer;
 import com.tzld.piaoquan.ad.engine.service.predict.param.ThresholdPredictModelParam;
 import org.slf4j.Logger;
@@ -21,23 +23,39 @@ public class RandomPredict667Model extends ThresholdPredictModel {
 
     @Override
     public Map<String, Object> predict(ThresholdPredictModelParam modelParam) {
-        int hash=modelParam.getMid().hashCode();
-        hash=hash<0?-hash:hash;
-        double score=(hash+ RandWContainer.getRandW())%100/100d;
-        double threshold=Double.parseDouble(
-                modelParam.getExtraParam().getOrDefault(modelParam.getAppType()+"_"+modelParam.getUserExtraFuture("shareType").toString().replace("return","").replace("mids",""),-1
-                ).toString());
-        if(threshold<0d){
-            threshold=Double.parseDouble(
-                    modelParam.getExtraParam().getOrDefault("default_threshold","0.5")
-                    .toString());
+        int hash = modelParam.getMid().hashCode();
+        hash = hash < 0 ? -hash : hash;
+        double score = (hash + RandWContainer.getRandW()) % 100 / 100d;
+
+        String thresholdParamKey = modelParam.getAppType() + "_" + modelParam.getUserExtraFuture("shareType").toString().replace("return", "").replace("mids", "");
+
+        double threshold = Double.parseDouble(modelParam.getExtraParam().getOrDefault(thresholdParamKey, -1).toString());
+
+        if (threshold < 0d) {
+            thresholdParamKey = "default_threshold";
+            threshold = Double.parseDouble(modelParam.getExtraParam().getOrDefault(thresholdParamKey, "0.5").toString());
         }
         Map<String, Object> result = new HashMap<>();
-        result.put("ad_predict", score<threshold?2:1);
+        result.put("ad_predict", score < threshold ? 2 : 1);
         result.put("score", score);
         result.put("threshold", threshold);
         result.put("model", initName());
 
+        JSONObject logJson = new JSONObject();
+        logJson.putAll(result);
+        logJson.put("mid", modelParam.getMid());
+        logJson.put("expId", "667");
+        logJson.put("appType", modelParam.getAppType());
+        logJson.put("thresholdParamKey", thresholdParamKey);
+        logJson.put("adPlatformType", modelParam.getAdPlatformType());
+        logJson.put("abCode", modelParam.getAbTestCode());
+        logJson.put("extraParam", modelParam.getExtraParam());
+        logJson.put("shareType", modelParam.getUserExtraFuture("shareType").toString());
+        logJson.putAll(result);
+
+        log.info("广告跳出选择 -- 667实验结果: {}, 参数: {}",
+                JSONUtils.toJson(result), logJson.toJSONString());
+
         return result;
     }
 

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/predict/model/threshold/RandomPredictModel.java

@@ -55,6 +55,7 @@ public class RandomPredictModel extends ThresholdPredictModel {
         logJson.put("appType", appType);
         logJson.put("thresholdParamKey", thresholdParamKey);
         logJson.put("adPlatformType", modelParam.getAdPlatformType());
+        logJson.put("abCode", modelParam.getAbTestCode());
 
         log.info("广告跳出选择 -- 599实验结果: {}, 参数: {}",
                 JSONUtils.toJson(result), logJson.toJSONString());

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/strategy/RankStrategyBasic.java

@@ -203,6 +203,17 @@ public abstract class RankStrategyBasic implements RankStrategy {
                 map.put("root_source_channel", rootSourceChannel);
             }
         }
+        Map<String, String> userLayer = getUserLayer(request.getMid());
+        String layer = userLayer.getOrDefault("layer", "无曝光");
+        String clazz = userLayer.getOrDefault("class", "近期未出现");
+        if (StringUtils.isNotEmpty(layer) && layer.equals("已转化")) {
+            layer = "有转化";
+        }
+        if (request.getIsFilterUser()) {
+            layer = layer + "-炸";
+        }
+        String userLayerClass = layer + "-" + clazz;
+        map.put("user_layer_class", userLayerClass);
         return map;
     }
 

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/strategy/RankStrategyBy688.java

@@ -145,6 +145,7 @@ public class RankStrategyBy688 extends RankStrategyBasic {
         userFeatureMap.put("user_vid_share_cate1_14d", g2Feature.getOrDefault("cate1_14d", null));
         userFeatureMap.put("user_vid_share_cate2_14d", g2Feature.getOrDefault("cate2_14d", null));
 
+        userFeatureMap.put("user_layer_class", reqFeature.getOrDefault("user_layer_class", null));
 
         Map<String, String> sceneFeatureMap = this.handleSceneFeature(ts);
         long time1 = System.currentTimeMillis();
@@ -860,6 +861,7 @@ public class RankStrategyBy688 extends RankStrategyBasic {
             add("user_skuid_view_30d");
             add("user_skuid_click_30d");
             add("user_skuid_conver_30d");
+            add("user_layer_class");
         }};
     }